{
  "abstract": "BackgroundExcess epicardial adipose tissue (EAT) is associated with cardiovascular-kidney-metabolic (CKM) dysfunction, but its assessment has traditionally required advanced imaging.\n\nObjectivesWe investigated whether AI-enabled echocardiography could characterize epicardial adiposity and identify residual cardiometabolic risk beyond body mass index (BMI).\n\nMethodsWe developed PanAdipo, a video-based deep learning model in 1,114,441 videos from 28,797 studies across the Yale-New Haven Health System (YNHHS; 2016-2022), using report-derived expert reader labels of prominent EAT (2.3% of studies). PanAdipo was evaluated in five cohorts: a temporally distinct YNHHS transthoracic echocardiography (TTE) set (n=4,588 patients contributing 5,130 studies), an emergency department point-of-care ultrasound (POCUS) cohort (n=10,957), the geographically distinct MIMIC-IV dataset (n=4,549), the community-based Multi-Ethnic Study of Atherosclerosis (MESA Exam 6, n=2,740), and in held-out YNHHS patients with paired coronary CT angiography within 12 months (n=5,167). Analyses examined discrimination of the report label and of CT-defined high indexed EAT volume, correlation with paired cardiac CT phenotypes, and BMI-adjusted associations with cardiometabolic biomarkers and newly documented metabolic disease.\n\nResultsIn the held-out health system test set, PanAdipo identified prominent EAT documented in clinical reports with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI, 0.82-0.91) across all views and of up to 0.93 when combining apical 4-chamber and parasternal views. Against paired CT-derived measurements, PanAdipo discriminated age- and sex-specific high EAT (EAT/heart volume [≥]95th percentile) with AUROCs of 0.91 to 0.95 across age-sex groups and correlated more strongly with indexed EAT volume ({rho}=0.76) than with BMI ({rho}=0.41, all P<0.001); PanAdipo alone explained 53% of indexed EAT volume variance, versus 23% for age, sex, and BMI combined. In MESA, higher scores were associated with an adverse metabolic profile (higher HOMA-IR [{beta}=0.17 per SD; 95% CI, 0.11-0.22]) after adjustment for age, sex, and BMI. In health system TTE and POCUS cohorts, higher scores were associated with newly documented MASLD/MASH (BMI-adjusted HRpooled 1.31 [1.03-1.67] per SD of log[PanAdipo]) and hyperlipidemia (1.11 [1.04-1.18]).\n\nConclusionsAI-enabled echocardiography provides a scalable digital biomarker that characterizes prominent epicardial adiposity and is associated with an adverse cardiometabolic profile, highlighting a role for echocardiography in metabolic risk stratification.\n\nCONDENSED ABSTRACTEpicardial adipose tissue (EAT) is linked to cardiometabolic risk, but its assessment has traditionally required advanced imaging. We developed PanAdipo, a deep learning model that derives a digital biomarker of EAT from routine echocardiography. PanAdipo tracks CT-defined epicardial adiposity ({rho}=0.76; AUROC 0.91-0.95 for age-/sex-specific high indexed-EAT), yet correlates modestly with body mass index (BMI; {rho}=0.19-0.41). After BMI adjustment, higher scores were associated with insulin resistance in a community-based cohort and newly documented metabolic disease across health systems. AI-enabled echocardiography may provide a scalable window into epicardial adiposity, with potential applications for enriching trial populations and monitoring response to cardiometabolic therapies.",
  "abstract_shown": true,
  "affiliations": [
    "Yale School of Medicine"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "A. Aminorroaya"
    },
    {
      "name": "A. Coppi"
    },
    {
      "name": "R. L. McNamara"
    },
    {
      "name": "J. A. C. Lima"
    },
    {
      "name": "H. M. Krumholz"
    },
    {
      "name": "C. Antoniades"
    },
    {
      "name": "R. Khera"
    },
    {
      "affs": [
        0
      ],
      "name": "E. K. Oikonomou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3p42r47g7dyr5.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.07.22.26358690",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.07.22.26358690v2.full",
  "id": "3p42r47g7dyr5",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.07.22.26358690v2",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "cardiovascular medicine"
    ],
    [
      "openalex",
      "2705|Cardiovascular Disease and Adiposity"
    ]
  ],
  "openalex": "W7170875842",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.07.22.26358690v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3p42r47g7dyr5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:34Z",
      "native_id": "10.64898/2026.07.22.26358690",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.07.22.26358690v2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:cardiovascular medicine",
      "subfield": "cardiovascular"
    }
  ],
  "title": "AI-Enabled Echocardiography Identifies Prominent Epicardial Adipose Tissue Associated With an Adverse Cardiometabolic Profile",
  "title_text": "AI-Enabled Echocardiography Identifies Prominent Epicardial Adipose Tissue Associated With an Adverse Cardiometabolic Profile",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:34Z",
  "url": "https://research.draconitic.com/paper/3p42r47g7dyr5",
  "venue": "medRxiv",
  "versions": []
}